{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview of this notebook\n",
    "\n",
    "* motivating example with modular building blocks\n",
    "    * connecting distances, aligners, classifiers\n",
    "* pairwise transformers - the \"type\" of time series distances and kernels\n",
    "* time series alignment and alignment distances, e.g., time warping\n",
    "* composition patterns for distances, kernels, aligners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Motivating example"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rich component relationships between object types!\n",
    "\n",
    "* many classifiers, regressors, clusterers use distances or kernels\n",
    "* distances and kernels are often composite, e.g., sum-of-distance, independent distance\n",
    "* TS distances are often based on scalar multivariate distances (e.g., Euclidean)\n",
    "* TS distances are often based on alignment, TS aligners are an estimator type!\n",
    "* aligners internally typically use scalar uni/multivariate distances"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "example:\n",
    "\n",
    "* 1-nn using `sklearn` nearest neighbors\n",
    "* with multivariate dynamic time warping distance, from `dtw-python` library \n",
    "* on multivariate `\"mahalanobis\"` distance from `scipy`\n",
    "* in `sktime` compatible interface, constructed from custom components"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so, conceptually:\n",
    "\n",
    "* we build an sequence alignment algorithm (`dtw-python`) using `scipy` Mahalanobis dist\n",
    "* we get the distance matrix computation from alignment algorithm\n",
    "* we use that distance matrix in `sklearn` knn\n",
    "* together this is a time series classifier!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.alignment.dtw_python import AlignerDTWfromDist\n",
    "from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier\n",
    "from sktime.dists_kernels.compose_from_align import DistFromAligner\n",
    "from sktime.dists_kernels.scipy_dist import ScipyDist\n",
    "\n",
    "# Mahalanobis distance on R^n\n",
    "mahalanobis_dist = ScipyDist(metric=\"mahalanobis\")  # uses scipy distances\n",
    "\n",
    "# pairwise multivariate aligner from dtw-python with Mahalanobis distance\n",
    "mw_aligner = AlignerDTWfromDist(mahalanobis_dist)  # uses dtw-python\n",
    "\n",
    "# turning this into alignment distance on time series\n",
    "dtw_dist = DistFromAligner(mw_aligner)  # interface mutation to distance\n",
    "\n",
    "# and using this distance in a k-nn classifier\n",
    "clf = KNeighborsTimeSeriesClassifier(distance=dtw_dist)  # uses sklearn knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'brute',\n",
       " 'distance': DistFromAligner(aligner=AlignerDTWfromDist(dist_trafo=ScipyDist(metric='mahalanobis'))),\n",
       " 'distance_mtype': None,\n",
       " 'distance_params': None,\n",
       " 'leaf_size': 30,\n",
       " 'n_jobs': None,\n",
       " 'n_neighbors': 1,\n",
       " 'pass_train_distances': False,\n",
       " 'weights': 'uniform',\n",
       " 'distance__aligner': AlignerDTWfromDist(dist_trafo=ScipyDist(metric='mahalanobis')),\n",
       " 'distance__aligner__dist_trafo': ScipyDist(metric='mahalanobis'),\n",
       " 'distance__aligner__open_begin': False,\n",
       " 'distance__aligner__open_end': False,\n",
       " 'distance__aligner__step_pattern': 'symmetric2',\n",
       " 'distance__aligner__window_type': 'none',\n",
       " 'distance__aligner__dist_trafo__colalign': 'intersect',\n",
       " 'distance__aligner__dist_trafo__metric': 'mahalanobis',\n",
       " 'distance__aligner__dist_trafo__metric_kwargs': None,\n",
       " 'distance__aligner__dist_trafo__p': 2,\n",
       " 'distance__aligner__dist_trafo__var_weights': None}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.get_params()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what are all the objects in this chain?\n",
    "\n",
    "* `ScipyDist` - pairwise distance between *scalars* - `transformer-pairwise` type\n",
    "* `AlignerDtwFromDist` - time series alignment algorithm - `aligner` type\n",
    "* `DistFromAligner`- pairwise distance between *time series* - `transformer-pairwise-panel` type\n",
    "* `KNeighborsTimeSeriesClassifier` - time series classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'aligner'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.registry import scitype\n",
    "\n",
    "scitype(mw_aligner)  # prints the type of estimator (as a string)\n",
    "# same for other components"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's go through these - we've already seen classifiers."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Time series distances and kernels - pairwise panel transformers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.1 Distances, kernels - general interface"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pairwise panel transformers produce one distance per pair of series in the panel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datasets import load_osuleaf\n",
    "\n",
    "# load an example time series panel in numpy mtype\n",
    "X, _ = load_osuleaf(return_type=\"numpy3D\")\n",
    "\n",
    "X1 = X[:3]\n",
    "X2 = X[5:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing the transformer\n",
    "from sktime.dists_kernels import FlatDist\n",
    "from sktime.dists_kernels.scipy_dist import ScipyDist\n",
    "\n",
    "# paired Euclidean distances, over time points\n",
    "eucl_dist = FlatDist(ScipyDist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1, 427)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1, 427)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X1 is panel with 3 series\n",
    "X2 is panel with 5 series\n",
    "\n",
    "so a matrix of pairwise distances from X1 to X2 should have shape (3, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[29.94033435, 30.69443315, 29.02704475, 30.49413394, 29.77534229],\n",
       "       [28.86289916, 32.03165025, 29.6118973 , 32.95499251, 30.82017584],\n",
       "       [29.52672336, 18.76259726, 30.55213501, 15.93324954, 27.89072122]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distmat = eucl_dist(X1, X2)\n",
    "\n",
    "# alternatively, via the transform method\n",
    "distmat = eucl_dist.transform(X1, X2)\n",
    "distmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distmat.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "call or `transform` with a single arg is the same as passing twice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        , 24.58470308, 33.83913255],\n",
       "       [24.58470308,  0.        , 35.44109497],\n",
       "       [33.83913255, 35.44109497,  0.        ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distmat_symm = eucl_dist.transform(X1)\n",
    "distmat_symm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pairwise panel transformers are `scikit-learn` / `scikit-base` interface compatible and composable, like everything else in `sktime`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'transformer': ScipyDist(),\n",
       " 'transformer__colalign': 'intersect',\n",
       " 'transformer__metric': 'euclidean',\n",
       " 'transformer__metric_kwargs': None,\n",
       " 'transformer__p': 2,\n",
       " 'transformer__var_weights': None}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eucl_dist.get_params()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.2 Time series distances, kernels - composition"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pairwise transformers can be composed in a number of ways:\n",
    "\n",
    "* arithmetics, e.g., addition, multiplication - use dunder `+`, `*` etc, or `CombinedDistance`\n",
    "* subset to one or multiple columns - use `my_dist[colnames]` dunder\n",
    "* sum or aggregate over univariate distance in multivariate panel, using `IndepDist` (also known as \"independent distance\")\n",
    "* compose with series-to-series transformers - use `*` dunder or `make_pipeline`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 6, 100)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.datasets import load_basic_motions\n",
    "\n",
    "# load an example time series panel in numpy mtype\n",
    "X, _ = load_basic_motions(return_type=\"numpy3D\")\n",
    "X = X[:3]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                 pw_trafos=[PwTrafoPanelPipeline(pw_trafo=FlatDist(transformer=ScipyDist()),\n",
       "                                                 transformers=[ColumnSelect(columns=4)]),\n",
       "                            PwTrafoPanelPipeline(pw_trafo=FlatDist(transformer=ScipyDist(metric=&#x27;cosine&#x27;)),\n",
       "                                                 transformers=[ColumnSelect(columns=2)])])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class='sk-label-container'><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('affd8392-12ba-44c6-9442-f5ece3da38dd') type=\"checkbox\" ><label for=UUID('affd8392-12ba-44c6-9442-f5ece3da38dd') class='sk-toggleable__label sk-toggleable__label-arrow'>CombinedDistance</label><div class=\"sk-toggleable__content\"><pre>CombinedDistance(operation=&#x27;*&#x27;,\n",
       "                 pw_trafos=[PwTrafoPanelPipeline(pw_trafo=FlatDist(transformer=ScipyDist()),\n",
       "                                                 transformers=[ColumnSelect(columns=4)]),\n",
       "                            PwTrafoPanelPipeline(pw_trafo=FlatDist(transformer=ScipyDist(metric=&#x27;cosine&#x27;)),\n",
       "                                                 transformers=[ColumnSelect(columns=2)])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('ca27dedd-376c-405b-81ea-330103c8bc33') type=\"checkbox\" ><label for=UUID('ca27dedd-376c-405b-81ea-330103c8bc33') class='sk-toggleable__label sk-toggleable__label-arrow'>ScipyDist</label><div class=\"sk-toggleable__content\"><pre>ScipyDist()</pre></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('26c36ab3-80fc-4157-a2ca-dcbca9277a5b') type=\"checkbox\" ><label for=UUID('26c36ab3-80fc-4157-a2ca-dcbca9277a5b') class='sk-toggleable__label sk-toggleable__label-arrow'>ColumnSelect</label><div class=\"sk-toggleable__content\"><pre>ColumnSelect(columns=4)</pre></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('6e85be01-0d20-4b05-a9c5-9f399e2c89bf') type=\"checkbox\" ><label for=UUID('6e85be01-0d20-4b05-a9c5-9f399e2c89bf') class='sk-toggleable__label sk-toggleable__label-arrow'>ScipyDist</label><div class=\"sk-toggleable__content\"><pre>ScipyDist(metric=&#x27;cosine&#x27;)</pre></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('da1bc488-94df-44be-a63c-034c33eac7d6') type=\"checkbox\" ><label for=UUID('da1bc488-94df-44be-a63c-034c33eac7d6') class='sk-toggleable__label sk-toggleable__label-arrow'>ColumnSelect</label><div class=\"sk-toggleable__content\"><pre>ColumnSelect(columns=2)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "CombinedDistance(operation='*',\n",
       "                 pw_trafos=[PwTrafoPanelPipeline(pw_trafo=FlatDist(transformer=ScipyDist()),\n",
       "                                                 transformers=[ColumnSelect(columns=4)]),\n",
       "                            PwTrafoPanelPipeline(pw_trafo=FlatDist(transformer=ScipyDist(metric='cosine')),\n",
       "                                                 transformers=[ColumnSelect(columns=2)])])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example 1: variable subsetting and arithmetic combinations\n",
    "\n",
    "# we define *two* distances now\n",
    "from sktime.dists_kernels import FlatDist, ScipyDist\n",
    "\n",
    "# Euclidean distance (on flattened time series)\n",
    "eucl_dist = FlatDist(ScipyDist())\n",
    "# Mahalanobis distance (on flattened time series)\n",
    "cos_dist = FlatDist(ScipyDist(metric=\"cosine\"))\n",
    "\n",
    "# arithmetic product of:\n",
    "# * the Euclidean distance on gyrometer 2 time series\n",
    "# * the Cosine distance on accelerometer 3 time series\n",
    "prod_dist_42 = eucl_dist[4] * cos_dist[2]\n",
    "prod_dist_42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.87274896, 2.28712525],\n",
       "       [1.87274896, 0.        , 2.62764453],\n",
       "       [2.28712525, 2.62764453, 0.        ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_dist_42(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example 2: independent dynamic time warping distance\n",
    "from sktime.alignment.dtw_python import AlignerDTW\n",
    "from sktime.dists_kernels.compose_from_align import DistFromAligner\n",
    "from sktime.dists_kernels.indep import IndepDist\n",
    "\n",
    "# dynamic time warping distance - this is multivariate\n",
    "dtw_dist = DistFromAligner(AlignerDTW())\n",
    "\n",
    "# independent distance - by default IndepDist sums over univariate distances\n",
    "indep_dtw_dist = IndepDist(dtw_dist)\n",
    "\n",
    "# that is, this distance is arithmetic sum of\n",
    "# * DTW distance on accelerometer 1 time series\n",
    "# * DTW distance on accelerometer 2 time series\n",
    "# * DTW distance on accelerometer 3 time series\n",
    "# * DTW distance on gyrometer 1 time series\n",
    "# * DTW distance on gyrometer 2 time series\n",
    "# * DTW distance on gyrometer 3 time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        , 31.7765985 , 32.65822   ],\n",
       "       [31.7765985 ,  0.        , 39.78652033],\n",
       "       [32.65822   , 39.78652033,  0.        ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indep_dtw_dist(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example 3: dynamic time warping distance on first differences\n",
    "from sktime.transformations.series.difference import Differencer\n",
    "\n",
    "diff_dtw_distance = Differencer() * dtw_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.      , 20.622806, 27.731956],\n",
       "       [20.622806,  0.      , 30.487498],\n",
       "       [27.731956, 30.487498,  0.      ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_dtw_distance(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "some combinations may be available as efficient `numba` based distances.\n",
    "\n",
    "E.g., difference-then-dtw is available as the \"fixed\" `sktime` native implementation\n",
    "`DtwDist(derivative=True)` in `sktime.dists_kernels.dtw`."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 pairwise tabular transformers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3.1 pairwise tabular transformers - general interface"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pairwise tabular transformers transform pairs of ordinary tabular data, e.g., plain `pd.DataFrame`\n",
    "\n",
    "produce one distance per pair of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datatypes import get_examples\n",
    "\n",
    "# we retrieve some DataFrame examples\n",
    "X_tabular = get_examples(\"pd.DataFrame\", \"Series\")[1]\n",
    "X2_tabular = get_examples(\"pd.DataFrame\", \"Series\")[1][0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>-0.428571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a         b\n",
       "0  1.0  3.000000\n",
       "1  4.0  7.000000\n",
       "2  0.5  2.000000\n",
       "3 -3.0 -0.428571"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# just an ordinary DataFrame, no time series\n",
    "X_tabular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b\n",
       "0  1.0  3.0\n",
       "1  4.0  7.0\n",
       "2  0.5  2.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2_tabular"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "example: pairwise Euclidean distance between rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing the transformer\n",
    "from sktime.dists_kernels import ScipyDist\n",
    "\n",
    "# mean of paired Euclidean distances\n",
    "my_tabular_dist = ScipyDist(metric=\"euclidean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  5.        ,  1.11803399],\n",
       "       [ 5.        ,  0.        ,  6.10327781],\n",
       "       [ 1.11803399,  6.10327781,  0.        ],\n",
       "       [ 5.26831112, 10.20704039,  4.26004216]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtain matrix of distances between each pair of rows in X_tabular, X2_tabular\n",
    "my_tabular_dist(X_tabular, X2_tabular)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  5.        ,  1.11803399],\n",
       "       [ 5.        ,  0.        ,  6.10327781],\n",
       "       [ 1.11803399,  6.10327781,  0.        ],\n",
       "       [ 5.26831112, 10.20704039,  4.26004216]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternative call with transform:\n",
    "my_tabular_dist.transform(X_tabular, X2_tabular)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  5.        ,  1.11803399,  5.26831112],\n",
       "       [ 5.        ,  0.        ,  6.10327781, 10.20704039],\n",
       "       [ 1.11803399,  6.10327781,  0.        ,  4.26004216],\n",
       "       [ 5.26831112, 10.20704039,  4.26004216,  0.        ]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as with pairwise panel transformers, one arg means second is the same\n",
    "my_tabular_dist(X_tabular)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3.2 constructing pairwise time series transformers from tabular ones"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"simple\" time series distances can be obtained directly from tabular transformers:\n",
    "\n",
    "* flattening the time series to tabular, and then computing the distance - `FlatDist`\n",
    "* aggregating the tabular distance matrix, from two individual time series - `AggrDist`\n",
    "\n",
    "these are important \"baseline\" distances!\n",
    "\n",
    "Both can be used on `sktime` pairwise transformers and `sklearn` pairwise transformers.\n",
    "\n",
    "the classes are called \"dist\" but all apply to kernels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 6, 100)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sktime.datasets import load_basic_motions\n",
    "\n",
    "# load an example time series panel in numpy mtype\n",
    "X, _ = load_basic_motions(return_type=\"numpy3D\")\n",
    "X = X[:3]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'transformer': RBF(length_scale=10),\n",
       " 'transformer__length_scale': 10,\n",
       " 'transformer__length_scale_bounds': (1e-05, 100000.0)}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example 1: flat Gaussian RBF kernel between time series\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "\n",
    "from sktime.dists_kernels import FlatDist\n",
    "\n",
    "flat_gaussian_tskernel = FlatDist(RBF(length_scale=10))\n",
    "flat_gaussian_tskernel.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.02267939, 0.28034066],\n",
       "       [0.02267939, 1.        , 0.05447445],\n",
       "       [0.28034066, 0.05447445, 1.        ]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_gaussian_tskernel(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'transformer': ScipyDist(metric='cosine'),\n",
       " 'transformer__colalign': 'intersect',\n",
       " 'transformer__metric': 'cosine',\n",
       " 'transformer__metric_kwargs': None,\n",
       " 'transformer__p': 2,\n",
       " 'transformer__var_weights': None}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example 2: pairwise cosine distance - we've already seen FlatDist a couple times\n",
    "from sktime.dists_kernels import FlatDist, ScipyDist\n",
    "\n",
    "cos_tsdist = FlatDist(ScipyDist(metric=\"cosine\"))\n",
    "cos_tsdist.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.11022302e-16, 1.36699314e+00, 6.99338545e-01],\n",
       "       [1.36699314e+00, 0.00000000e+00, 1.10061843e+00],\n",
       "       [6.99338545e-01, 1.10061843e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos_tsdist(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.4 alignment algorithms, aka aligners"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* \"aligners\" find a new index set for 2 or more time series so they become \"similar\"\n",
    "* new index set being a non-linear reparameterization of the old index sets\n",
    "* often, aligners also produce an overall distance between the two series"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4.1 aligners - general interface"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "aligner methods:\n",
    "\n",
    "* `fit` - computes alignment\n",
    "* `get_alignment` - returns reparameterized indices, also called \"alignment path\"\n",
    "* `get_aligned` returns reparameterized series\n",
    "* `get_distance` returns distance between the two aligned series - only available if `\"capability:get_distance\"`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's try to align two leaf contours from OSUleaf!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OSUleaf is a panel dataset with flattened tree leaf contours\n",
    "\n",
    "* instance = leaf\n",
    "* index (\"time\") = angle from barycenter\n",
    "* variable = contour distance from barycenter at that angle\n",
    "\n",
    "![](./img/verdena_shapelet.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datasets import load_osuleaf\n",
    "\n",
    "# load an example time series panel in numpy mtype\n",
    "X, _ = load_osuleaf(return_type=\"pd-multiindex\")\n",
    "\n",
    "X1 = X.loc[0]  # leaf 0\n",
    "X2 = X.loc[1]  # leaf 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sktime.utils.plotting import plot_series\n",
    "\n",
    "plot_series(X1, X2, labels=[\"leaf_1\", \"leaf_2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.alignment.dtw_python import AlignerDTW\n",
    "\n",
    "# use dtw-python package for aligning\n",
    "# simple univariate alignment algorithm with default params\n",
    "aligner = AlignerDTW()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-044e1d9e-559c-433d-b826-8d52c0de7727 {color: black;background-color: white;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 pre{padding: 0;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-toggleable {background-color: white;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-estimator:hover {background-color: #d4ebff;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-item {z-index: 1;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-parallel-item:only-child::after {width: 0;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-044e1d9e-559c-433d-b826-8d52c0de7727 div.sk-text-repr-fallback {display: none;}</style><div id='sk-044e1d9e-559c-433d-b826-8d52c0de7727' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AlignerDTW()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('662c171d-6d08-4105-afa9-33a1eafbf6a3') type=\"checkbox\" checked><label for=UUID('662c171d-6d08-4105-afa9-33a1eafbf6a3') class='sk-toggleable__label sk-toggleable__label-arrow'>AlignerDTW</label><div class=\"sk-toggleable__content\"><pre>AlignerDTW()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AlignerDTW()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aligner.fit([X1, X2])  # series to align need to be passed as list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ind0</th>\n",
       "      <th>ind1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>423</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>424</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>425</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>426</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>426</td>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>668 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ind0  ind1\n",
       "0       0     0\n",
       "1       0     1\n",
       "2       0     2\n",
       "3       1     2\n",
       "4       2     3\n",
       "..    ...   ...\n",
       "663   423   422\n",
       "664   424   423\n",
       "665   425   424\n",
       "666   426   425\n",
       "667   426   426\n",
       "\n",
       "[668 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alignment path\n",
    "aligner.get_alignment()\n",
    "\n",
    "# this aligns, e.g.:\n",
    "# from row \"2\": aligns index 0 in X1 with index 2 of X2\n",
    "# from row \"664\": aligns index 424 in X1 with index 423 of X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain the aligned versions of the two series\n",
    "X1_al, X2_al = aligner.get_aligned()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1600x400 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sktime.utils.plotting import plot_series\n",
    "\n",
    "plot_series(\n",
    "    X1_al.reset_index(drop=True),\n",
    "    X2_al.reset_index(drop=True),\n",
    "    labels=[\"leaf_1\", \"leaf_2\"],\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the DTW aligner has a \"distance\" implemented\n",
    "\n",
    "intuitively, it is a distance that sums distance after aligning, and the amount of stretch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'python_dependencies_alias': {'dtw-python': 'dtw'},\n",
       " 'capability:multiple-alignment': False,\n",
       " 'capability:distance': True,\n",
       " 'capability:distance-matrix': True,\n",
       " 'python_dependencies': 'dtw-python'}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the AlignerDTW class (based on dtw-python) doesn't just align\n",
    "# it also produces a distance\n",
    "aligner.get_tags()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113.73231668301005"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the distance between the two time series we aligned\n",
    "aligner.get_distance()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4.2 alignment based time series distances"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the `DistFromAligner` wrapper simply computes distance per pair of aligned series.\n",
    "\n",
    "This turns any aligner into a time series distance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.alignment.dtw_python import AlignerDTW\n",
    "from sktime.dists_kernels.compose_from_align import DistFromAligner\n",
    "\n",
    "# dynamic time warping distance - this is multivariate\n",
    "dtw_dist = DistFromAligner(AlignerDTW())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.datasets import load_osuleaf\n",
    "\n",
    "# load an example time series panel in numpy mtype\n",
    "X, _ = load_osuleaf(return_type=\"numpy3D\")\n",
    "\n",
    "X1 = X[:3]\n",
    "X2 = X[5:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[165.25420136, 148.53521913, 159.93034065, 158.50379563,\n",
       "        155.98824527],\n",
       "       [153.5587322 , 151.52004769, 125.14570395, 183.97186106,\n",
       "         93.55389512],\n",
       "       [170.41354799, 154.24275848, 212.54601605,  66.59572457,\n",
       "        295.32544676]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtw_distmat = dtw_dist(X1, X2)\n",
    "dtw_distmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtw_distmat.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.5 revisiting the initial example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.alignment.dtw_python import AlignerDTWfromDist\n",
    "from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier\n",
    "from sktime.dists_kernels.compose_from_align import DistFromAligner\n",
    "from sktime.dists_kernels.scipy_dist import ScipyDist\n",
    "\n",
    "# Mahalanobis distance on R^n\n",
    "mahalanobis_dist = ScipyDist(metric=\"mahalanobis\")  # uses scipy distances\n",
    "\n",
    "# pairwise multivariate aligner from dtw-python with Mahalanobis distance\n",
    "mw_aligner = AlignerDTWfromDist(mahalanobis_dist)  # uses dtw-python\n",
    "\n",
    "# turning this into alignment distance on time series\n",
    "dtw_dist = DistFromAligner(mw_aligner)  # interface mutation to distance\n",
    "\n",
    "# and using this distance in a k-nn classifier\n",
    "clf = KNeighborsTimeSeriesClassifier(distance=dtw_dist)  # uses sklearn knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 {color: black;background-color: white;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 pre{padding: 0;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-toggleable {background-color: white;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-item {z-index: 1;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-parallel-item:only-child::after {width: 0;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1 div.sk-text-repr-fallback {display: none;}</style><div id='sk-56ed1cee-6770-4e2a-879c-faaa3bcb3eb1' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsTimeSeriesClassifier(distance=DistFromAligner(aligner=AlignerDTWfromDist(dist_trafo=ScipyDist(metric=&#x27;mahalanobis&#x27;))))</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class='sk-label-container'><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('5aed8853-66e7-46ab-b265-ec99cd14e857') type=\"checkbox\" ><label for=UUID('5aed8853-66e7-46ab-b265-ec99cd14e857') class='sk-toggleable__label sk-toggleable__label-arrow'>KNeighborsTimeSeriesClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsTimeSeriesClassifier(distance=DistFromAligner(aligner=AlignerDTWfromDist(dist_trafo=ScipyDist(metric=&#x27;mahalanobis&#x27;))))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('9ab46e50-393d-43fd-928a-d934e5e69cf6') type=\"checkbox\" ><label for=UUID('9ab46e50-393d-43fd-928a-d934e5e69cf6') class='sk-toggleable__label sk-toggleable__label-arrow'>ScipyDist</label><div class=\"sk-toggleable__content\"><pre>ScipyDist(metric=&#x27;mahalanobis&#x27;)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsTimeSeriesClassifier(distance=DistFromAligner(aligner=AlignerDTWfromDist(dist_trafo=ScipyDist(metric='mahalanobis'))))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* we build an sequence alignment algorithm (`dtw-python`) using `scipy` Mahalanobis dist\n",
    "* we get the distance matrix computation from alignment algorithm\n",
    "* we use that distance matrix in `sklearn` knn\n",
    "* together this is a time series classifier!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.6 Searching for distances, kernels, transformers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with all `sktime` objects, we can use the `registry.all_estimators` utility to display all transformers in `sktime`.\n",
    "\n",
    "The relevant scitypes are:\n",
    "\n",
    "* `\"transformer-pairwise\"` for all pairwise transformers on tabular data\n",
    "* `\"transformer-panel\"` for all pairwise transformers on panel data\n",
    "* `\"aligner\"` for all time series aligners\n",
    "* `\"transformer\"` for all transformers, these can be composed with all the aboev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sktime.registry import all_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AggrDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.compose_tab_to_pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CombinedDistance</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.algebra.CombinedD...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ConstantPwTrafoPanel</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dummy.ConstantPwT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DistFromAligner</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.compose_from_alig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DistFromKernel</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dist_to_kern.Dist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DtwDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dtw.DtwDist'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EditDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.edit_dist.EditDist'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FlatDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.compose_tab_to_pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IndepDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.indep.IndepDist'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KernelFromDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.dist_to_kern.Kern...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PwTrafoPanelPipeline</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.compose.PwTrafoPa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SignatureKernel</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.signature_kernel....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "                    name                                             object\n",
       "0               AggrDist  <class 'sktime.dists_kernels.compose_tab_to_pa...\n",
       "1       CombinedDistance  <class 'sktime.dists_kernels.algebra.CombinedD...\n",
       "2   ConstantPwTrafoPanel  <class 'sktime.dists_kernels.dummy.ConstantPwT...\n",
       "3        DistFromAligner  <class 'sktime.dists_kernels.compose_from_alig...\n",
       "4         DistFromKernel  <class 'sktime.dists_kernels.dist_to_kern.Dist...\n",
       "5                DtwDist         <class 'sktime.dists_kernels.dtw.DtwDist'>\n",
       "6               EditDist  <class 'sktime.dists_kernels.edit_dist.EditDist'>\n",
       "7               FlatDist  <class 'sktime.dists_kernels.compose_tab_to_pa...\n",
       "8              IndepDist     <class 'sktime.dists_kernels.indep.IndepDist'>\n",
       "9         KernelFromDist  <class 'sktime.dists_kernels.dist_to_kern.Kern...\n",
       "10  PwTrafoPanelPipeline  <class 'sktime.dists_kernels.compose.PwTrafoPa...\n",
       "11       SignatureKernel  <class 'sktime.dists_kernels.signature_kernel...."
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# listing all pairwise panel transformers - distances, kernels on time series\n",
    "all_estimators(\"transformer-pairwise-panel\", as_dataframe=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ScipyDist</td>\n",
       "      <td>&lt;class 'sktime.dists_kernels.scipy_dist.ScipyD...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name                                             object\n",
       "0  ScipyDist  <class 'sktime.dists_kernels.scipy_dist.ScipyD..."
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# listing all pairwise (tabular) transformers - distances, kernels on vectors/df-rows\n",
    "all_estimators(\"transformer-pairwise\", as_dataframe=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AlignerDTW</td>\n",
       "      <td>&lt;class 'sktime.alignment.dtw_python.AlignerDTW'&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AlignerDTWfromDist</td>\n",
       "      <td>&lt;class 'sktime.alignment.dtw_python.AlignerDTW...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AlignerDtwNumba</td>\n",
       "      <td>&lt;class 'sktime.alignment.dtw_numba.AlignerDtwN...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name                                             object\n",
       "0          AlignerDTW   <class 'sktime.alignment.dtw_python.AlignerDTW'>\n",
       "1  AlignerDTWfromDist  <class 'sktime.alignment.dtw_python.AlignerDTW...\n",
       "2     AlignerDtwNumba  <class 'sktime.alignment.dtw_numba.AlignerDtwN..."
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# listing all alignment algorithms that can produce distances\n",
    "all_estimators(\"aligner\", as_dataframe=True, filter_tags={\"capability:distance\": True})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.7 Outlook, roadmap - panel tasks\n",
    "\n",
    "* implementing estimators - distances, classifiers, etc\n",
    "* backend optimizations - `numba`, distributed/parallel\n",
    "* sequence-to-sequence regression, classification\n",
    "* further maturing the time series alignment module\n",
    "\n",
    "join and contribute!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.8 Summary"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* `sktime` - modular framework for learning with time series\n",
    "* panel data = collections of time series - tasks classification, regression, clustering\n",
    "* build flexible pipelines with transformers, tune via grid search etc\n",
    "* panel estimators typically rely on time series distances, kernels, aligners\n",
    "* TS distances, kernels, aligners can also be constructed in modular, flexible way\n",
    "* all objects above are first-class citiziens with `sklearn`-like interface!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Credits: notebook 6 - time series distances, kernels, alignment\n",
    "\n",
    "notebook creation: fkiraly"
   ]
  }
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